By Evan McGonagill

October 24, 2018

Joules is a UK-based fashion retailer that has grown from a small market store in the early 90s to a global, multi-channel retailer with over 100 stores in the UK alone. The brand now has a strong US presence through wholesale relationships with major retailers such as Dillards, Nordstrom and others.

Ecommerce represents a significant share of Joules' revenue, at 35%—but it’s also the fastest growing area of business, expanding by 20-25% year on year. With that in mind, the brand has worked hard to stay ahead of the curve in ecommerce experience, keeping the quality of their digital interactions consistent with the smooth experiences they strive to offer in other channels.

Joules Wanted to Make Their Customers Feel Valued

The brand places tremendous importance on offering highly personalised interactions that make their customers feel valued. “We want to be recognized by our consumers as giving them relevant experiences that are more personal,” says Jas Chana, customer experience and analytics manager at Joules.

And Joules has a strategy for delivering on that promise: “Everyone wants to get to 1-to-1 personalisation, and for us the most scalable way to do that is through recommendations,” shares Jas. “No matter the channel via our ecommerce site, via clienteling and styling, in-store experiences, and even through what we’re sending out in our parcels— recommendations will be a cornerstone of how we provide personalised content to our consumers.”

Optimising the Experience with a Product Recommendations Solution

Joules selected Monetate Intelligent Recommendations™ to offer the finely-tuned experience that they feel their customers deserve after having tried other solutions that failed to impress. “We previously used a black box recommendations engine,” shares Jas, and “it wasn’t taking into account important data in its decisioning.” Instead, it relied on canned “wisdom of the crowd” recommendations that often fell flat, failing to present relevant product recommendations to the consumer.

The Joules marketing team lost faith in recommendations. It wasn’t until they looked at other websites to understand what their competitors were doing that they realized recommendations offered greater possibilities when supported by a strong technology partner like Monetate.

Joules knows their customers well, and they have the capacity to make good use of their data given the chance. Using Monetate’s recommendations solution allows them to leverage the “rich dataset” in their CRM, alongside “the behavioural context of what consumers are doing in the moment.” This means that decisions for each visit can be built on knowledge of a given customer’s most frequently purchased categories, location, device type, referral channel, and more. Each of these factors might be uniquely influential.

For example, they have observed that their customers on mobile are looking for faster, transaction-specific interactions than the ones they experience when visiting from other devices. Knowing this, and using a solution that can take it into account, allows Joules to more precisely target their audience segments with products that are right for them in the moment.

How Monetate Made These Changes Possible

Such intricate audience precision would be unthinkable without the help of automation. As Jas explains, “we wouldn’t be able to create curated looks for every product we have on the website; that would be an impossible undertaking.” They would need to build endless product sets with the necessary breadth to cover gaps due to out-of-stock items, and manually upload a spreadsheet for each dataset. Such a “process-heavy” workflow simply isn’t realistic for the business without an AI assist from Monetate.

Therefore, a combination of automated recommendations and manual styling feels just right. To begin, Joules has lined up two use cases—one of which is already up and running. They have implemented Individual Fit Experiences™ with the recommendations on the basket page, which make individualised recommendations based on insights about the product the customer has in their basket and knowledge about the customer context.

Next, they will roll out an experience that tests a combination of Individual Fit recommendations and “complete the look” curated selections. Eventually, they plan to use both AI-powered recommendations and manual curation on their product pages in order to reap the benefits of both methods. In addition to tracking AOV, the brand will measure the success of the programme by tracking customer loyalty and satisfaction overall, as they see recommendations as integral to those two all-important goals.

What the Future Holds for Joules

And the early indications are good. The team is excited to see that the Individual Fit recommendations applied to the basket have already resulted in a 3% lift in just 4 weeks —a strong signal of the “massive opportunity” that the team expects from continued experimentation with the platform.

“Monetate has become a major enabler to us being able to deliver great experiences to our customers, through both the technology they provide, and the strategy and direction that they help us develop for our business,” Jas says. We are equally excited to support Joules in their personalisation journey, as they explore the potential of automated recommendations for strong business impact and an exceptional customer experience.

If you would like to learn more about getting started with Monetate Intelligent Recommendations™, contact us today to speak with an expert

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Evan McGonagill is a content writer for Monetate, where she researches and produces whitepapers, blog posts, and other material about commerce and personalization. Evan has a background in libraries and archives, and she uses her interest in the structure and flow of information to think about how brands can harness data to build more personal connections with their customers. When she isn't in the library or learning about personalization, you can find her playing music in West Philadelphia.